Thirteen of fourteen models named Microsoft Power BI first on the direct prompt; one named Domo. Microsoft Power BI was named by fourteen of the fourteen models and Domo by ten and Microsoft Power BI carries 76 labels and Domo 22, so the shares are not directly comparable.
By Microsoft, Vancouver, Canada, founded 1975. Named in four categories this edition.
American Fork, United States, founded 2010. Named in four categories this edition.
Share is the count of first choices across the direct, paraphrase, budget and scale prompts over all fourteen models, for a mid-market B2B company; rank is within the category; every quote names the model and the prompt it came from. Both figures come from the dashboards and data visualization page.
Across every category in the November 2026 Edition, Microsoft Power BI and Domo were named in the same answer fifty-nine times, of the 281 answers naming Microsoft Power BI and the 73 naming Domo. In those answers Domo took the first choice zero times and Microsoft Power BI thirty-five.
| Model | Direct | Paraphrase | Comparative | Budget-constrained | Scale-constrained | Negative |
|---|---|---|---|---|---|---|
| Claude Haiku 4.5 | ||||||
| GPT-5.4 mini | ||||||
| Gemini 3.5 Flash | ||||||
| Perplexity Sonar | ||||||
| Grok 4.20 | ||||||
| Mistral Small | ||||||
| DeepSeek V4 Flash | ||||||
| Llama 4 Maverick | ||||||
| Qwen 3.7 Flash | ||||||
| Kimi K2 | ||||||
| GLM 4.7 FlashX | ||||||
| MiniMax M2.5 | ||||||
| GPT-6 Luna | ||||||
| Muse Glimmer 30B |
Bold names in an answer are the products the judge labeled a first choice; a model naming several gives each of them that label. The full answer text for every row is in the record.
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of eight in this category shown.
“Extremely popular and cost-effective, but watch for specialist needs (like building complex models), reconciliation errors, and AI that doesn't always meet hype.” Grok 4.20 · negative prompt · soft negative
“Power BI deeply locks you into Microsoft and can struggle with performance and governance when querying massive, complex datasets” Claude Haiku 4.5 · negative prompt · soft negative
“Power BI, for example, has meaningful differences between Free, Pro, and PPU, and sharing/collaboration can require paid licenses” GPT-5.4 mini · negative prompt · soft negative
“The strongest general-purpose choices are Microsoft Power BI, Tableau, Looker, Qlik, ThoughtSpot, Sigma, Sisense, Domo, Metabase, and Zoho Analytics.” Perplexity Sonar · comparative prompt · first choice
“Microsoft Power BI is the most-deployed mid-market BI platform globally... Power BI is the lowest-friction, cost-predictable default.” Muse Glimmer 30B · direct prompt · first choice
“Default / cost-effective for most mid-sized B2B: Microsoft Power BI. Strong Microsoft integration, Excel familiarity, low entry price” Muse Glimmer 30B · paraphrase prompt · first choice
Every negative label with a quote, up to three, then the highest-weighted positives, up to three. Six of seven in this category shown.
“Avoid or Consider Carefully: Domo: Consumption-based pricing can be unpredictable” Kimi K2 · paraphrase prompt · hard negative
“Products such as Sisense, Domo, and Qlik may be capable, but quote-led pricing makes total cost and renewal exposure harder to assess.” Perplexity Sonar · negative prompt · soft negative
“Opaque and potentially expensive pricing (not transparent, scales poorly for larger needs). Steep learning curve” Grok 4.20 · negative prompt · soft negative
“Start with Power BI or Domo depending on your specific situation” MiniMax M2.5 · direct prompt · first choice
“Choose Domo when rapid integration of many business data sources is more important than maximum visualization sophistication.” Perplexity Sonar · comparative prompt · alternative
“Choose Domo if: You are a marketing team or an SME that needs a mobile-friendly, all-in-one cloud platform” GLM 4.7 FlashX · comparative prompt · alternative
Comparisons are drawn for the top eight products in each category, each against each. The output is the models' output; nothing here is a recommendation by the index.